Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)

Vahid Alizadeh Sahzabi; Iman Karimi; Navid Alizadeh Sahzabi; Peiman Mamaani Barnaghi

doi:10.1117/12.921211

13 January 2012 Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)

Vahid Alizadeh Sahzabi, Iman Karimi, Navid Alizadeh Sahzabi, Peiman Mamaani Barnaghi

Proceedings Volume 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis; 83493H (2012) https://doi.org/10.1117/12.921211
Event: Fourth International Conference on Machine Vision (ICMV 11), 2011, Singapore, Singapore

Abstract

In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.

Citation Download Citation

Vahid Alizadeh Sahzabi, Iman Karimi, Navid Alizadeh Sahzabi, and Peiman Mamaani Barnaghi "Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)", Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83493H (13 January 2012); https://doi.org/10.1117/12.921211

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